Discovering customer segments through interaction behaviors for home appliance business

Market segmentation has become a crucial step in developing a successful marketing strategy, as it allows businesses to understand their target audience on a deeper level and create personalized experiences for different customer groups. For customer cluster analysis, this study expanded the customer base of existing research from market segmentation studies based on customer demographics and purchasing behavior to include pre-purchase perception/exploration as well as post-purchase actual usage data. To this end, all interactions with customers were designed based on CEJ (Customer Experience Journey), and then interaction characteristic variables were created. Here, we applied two data mining algorithms, clustering and NMF (Non-negative Matrix Factorization), based on the data of 40,911 actual customers in the home appliance business using the designed multidimensional behavioral characteristic variables. We conducted an evaluation study on the validity of cluster analysis by evaluating both the accuracy and stability (consistency) of the resulting customer segments from the two methods. Through analyzing the key characteristic data of the resulting customer segments, we investigated the technical advantages of each method and overlap/deviation of the resulting segments to provide analysis results. Thus, companies can focus their resources and efforts on the most profitable and receptive consumer groups by identifying the customers into distinct segments, as well as discover new business opportunities by figuring out less common but peculiar customer behaviors.